Deep learning for characterizing the self-assembly of three-dimensional colloidal systems
Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems’ stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. In this work, we first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.
- Research Organization:
- Lehigh Univ., Bethlehem, PA (United States); Univ. of California, Oakland, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- SC0013979; AC02-05CH11231; MCB120014
- OSTI ID:
- 1851853
- Alternate ID(s):
- OSTI ID: 2325164
- Journal Information:
- Soft Matter, Vol. 17, Issue 4; ISSN 1744-683X
- Publisher:
- Royal Society of ChemistryCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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